Getting started with deep learning involves more than learning what neural networks are: it also means meeting the programming and mathematical ideas that help explain how they work. Joe Grant’s guide takes a broad introductory route, beginning with Python and then expanding into artificial intelligence, machine learning, neural networks, and their applications.
The book is aimed at readers curious about AI who want an overview of both its foundations and its place in fields such as healthcare, banking, and manufacturing.
Start with Python, then move toward AI
The opening chapters introduce Python concepts including variables, data types, lists, and tuples, followed by functions and classes. This programming groundwork leads into explanations of artificial intelligence, machine learning, and deep-learning models, giving newcomers a sequence of topics to follow as the subject grows more technical.
Make sense of the building blocks
To help frame how machine-learning systems represent information, the book introduces mathematical terms such as scalars, vectors, matrices, and three-dimensional tensors. It also turns to artificial neural networks, their structure and history, and the role of Keras. The contents include a chapter on convolutional neural networks and building a CNN.
See where the ideas are applied
Alongside technical foundations, Grant surveys AI applications in healthcare, retail, banking, manufacturing, and travel. A separate section explores business intelligence—its processes, functions, users, and advantages—adding an organizational perspective to the discussion of intelligent systems.
A broad first look at machine learning
This English-language ebook may interest beginners who want an accessible orientation to Python and AI terminology before choosing a more specialized area to study. Its range—from programming basics and mathematical concepts to neural networks and business intelligence—offers several entry points for readers exploring the field.
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